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    <!-- <center><span style="font-size:44px;font-weight:bold;"></span></center><br/> -->
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    <center><span style="font-size:28px;font-weight:bold;">Deciphering Environmental Air Pollution with Large Scale City Data</span>
    </center><br />
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            <td align=center width=270px>
                <center><span style="font-size:18px"><a href="https://mayukh18.github.io/" target="_blank">Mayukh Bhattacharyya<sup>1</sup>*</a></span></center>
            </td>

            <td align=center width=230px>
                <center><span style="font-size:18px"><a
                            href="https://sayannag.github.io/"
                            target="_blank">Sayan Nag<sup>2</sup>*</a></span></center>
            </td>

            <td align=center width=200px>
                <center><span style="font-size:18px"><a href=""
                            target="_blank">Udita Ghosh<sup>3</sup></a></span></center>
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            <!--        <center><span style="font-size:20px">SketchX, CVSSP, University of Surrey, United Kingdom</span></center></td>-->
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            <!--        <center><span style="font-size:20px">CMU</span></center></td>-->
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            <!--        <center><span style="font-size:20px">UIUC</span></center></td>-->
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                        <center><span style="font-size:18px"><sup>1</sup>Stony Brook University
                                <br /> <sup>2</sup>University of Toronto
								<br /> <sup>3</sup>Zendrive Inc
								<br>
								<br /> * denotes equal contribution</span></center>
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                        <center><span style="font-size:18px"><span style="color:red"><strong>Spotlight</strong></span> and <strong>Oral</strong> at <a href="https://www.ijcai.org/">International Joint Conference on Artificial Intelligence (IJCAI)
                                    2022</a> </span></center>
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                        <center><span style="font-size:18px"><a
                                    href="https://arxiv.org/pdf/2109.04572.pdf">[Paper]</a></span></center>
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                        <center><span style="font-size:18px"><a
                                    href="https://github.com/mayukh18/DEAP/">[Code]</a></span>
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                        <center><span style="font-size:18px"><a
                                    href="data/pdf/ijcai 2022 slides.pdf">[Slides]</a></span>
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                                    href="">[Video]</a></span>
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            <hr>
			
			<div style="width:800px; margin:0 auto; text-align=justify"><center><span style="font-size:22px"><b>Abstract</b></span></center></div>
			
            <div style="width:800px; margin:0 auto; text-align=justify">
                Air pollution poses a serious threat to sustainable environmental conditions in the 21st century.
				Its importance in determining the health and living standards in urban settings is only expected to increase with time.
				Various factors ranging from artificial emissions to natural phenomena are known to be primary causal agents or
				influencers behind rising air pollution levels. However, the lack of large scale data involving the major artificial
				and natural factors has hindered the research on the causes and relations governing the variability of the different
				air pollutants. Through this work, we introduce a large scale city-wise dataset for exploring the relationships among
				these agents over a long period of time. We also introduce a transformer based model - <b>cosSquareFormer</b>, for the problem
				of pollutant level estimation and forecasting. Our model outperforms most of the benchmark models for this task.
				We also analyze and explore the dataset through our model and other methodologies to bring out important inferences
				which enable us to understand the dynamics of the casual agents at a deeper level. Through our paper, we seek to provide
				a great set of foundations for further research into this domain that will demand critical attention of ours in the near future.
            </div><br />
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                        <center><a href="data/images/fig1.png"><img src="data/images/fig1.png"
                                    width="500px"></img></a><br>
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            <br />
			
			<hr>
			
			<div style="width:800px; margin:0 auto; text-align=justify"><center><span style="font-size:22px"><b>Results</b></span></center></div>
			
            <div style="width:800px; margin:0 auto; text-align=justify">
                Table below shows the performance of predictions from different models for all 6 pollutants. LSTM E and Attention LSTM E are trained on explicit
information of weekday and month whereas the explicit information have been excluded while training the remaining models.
            </div><br />
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						<br>
						<div style="width:800px; margin:0 auto; text-align=justify">
                In the figures below, it can be observed that our proposed model does a great job in following
sudden daily fluctuations in the pollutant levels.
            </div><br />
						<center><a href="data/images/fig1.png"><img src="data/images/las_vegas_no2_1.png"
                                    width="330px"></img><img src="data/images/las_vegas_pm25_1.png"
                                    width="330px"></img></a><br>
                        </center>
						<br>
						<div style="width:800px; margin:0 auto; text-align=justify">
                In order to explore the sequential nature of pollutants, we designed
an ablation study with multiple sequence lengths with
the same experimental setup to maintain parity for modeling.
The results given in the figure below (left) show that pollutants like PM2.5,
PM10 and NO<sub>2</sub> have a better performance with longer sequence
lengths, whereas the others either degrade or show a
flat trend. Thus it can be assumed that the daily concentration
of some pollutants indeed have a good dependence on past
concentrations whereas some others are mostly independent
of it.
<br><br>
The visualizations shown in the figure below (right) provide some information
about each city’s conformity with the universal model.
It shows us the cities which have pollutant levels which were
much higher than that estimated by our model. It provides us
the leads to explore the context and reason behind each such
outlier city. An analysis on this basis will provide researchers
to identify problematic cases in a meaningful way instead of just flagging cities with high pollutant levels.
            </div><br />
						<center><a href="data/images/fig1.png"><img src="data/images/rmse_cossqformer_ablation_rRMSE_new.png"
                                    width="330px" height="220px"></img><img src="data/images/cities_over_polluted.png"
                                    width="330px"></img></a><br>
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                        <!--            Chaplot, D.S., Gandhi, D., Gupta, S., Gupta, A. and Salakhutdinov, R., 2020. Learning To Explore Using Active Neural SLAM. In International Conference on Learning Representations (ICLR).-->
                        <p style="text-align:left;"><center><b><span style="font-size:16pt">Citation</span></b></center><span
                                style="font-size:6px;">&nbsp;<br /></span> <span style="font-size:12pt"> Deciphering Environmental Air Pollution with Large Scale City Data. In
                                IJCAI 2022.</span></p>
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